Correlation between RPPA expression and clinical features
Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C16H4GR8
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between RPPA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C16H4GR8
Overview
Introduction

This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features. The input file "DLBC-TP.rppa.txt" is generated in the pipeline RPPA_AnnotateWithGene in the stddata run.

Summary

Testing the association between 192 genes and 8 clinical features across 33 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one genes.

  • 3 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • SQSTM1|P62-LCK-LIGAND ,  TTF1|TTF1 ,  YBX1|YB-1_PS102

  • 5 genes correlated to 'GENDER'.

    • EEF2|EEF2 ,  NF2|NF2 ,  RPS6KA1|P90RSK_PT359_S363 ,  ESR1|ER-ALPHA ,  RAF1|C-RAF_PS338

  • 4 genes correlated to 'RADIATION_THERAPY'.

    • CHEK2|CHK2_PT68 ,  CHEK2|CHK2 ,  NFKB1|NF-KB-P65_PS536 ,  STAT5A|STAT5-ALPHA

  • 13 genes correlated to 'RACE'.

    • PEA15|PEA15 ,  PXN|PAXILLIN ,  YAP1|YAP_PS127 ,  CCNE1|CYCLIN_E1 ,  FOXM1|FOXM1 ,  ...

  • 1 gene correlated to 'ETHNICITY'.

    • CHEK2|CHK2

  • No genes correlated to 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', and 'HISTOLOGICAL_TYPE'.

Results
Overview of the results

Complete statistical result table is provided in Supplement Table 1

Table 1.  Get Full Table This table shows the clinical features, statistical methods used, and the number of genes that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.

Clinical feature Statistical test Significant genes Associated with                 Associated with
DAYS_TO_DEATH_OR_LAST_FUP Cox regression test N=3   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test   N=0        
TUMOR_TISSUE_SITE Kruskal-Wallis test   N=0        
GENDER Wilcoxon test N=5 male N=5 female N=0
RADIATION_THERAPY Wilcoxon test N=4 yes N=4 no N=0
HISTOLOGICAL_TYPE Kruskal-Wallis test   N=0        
RACE Wilcoxon test N=13 white N=13 asian N=0
ETHNICITY Wilcoxon test N=1 not hispanic or latino N=1 hispanic or latino N=0
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

3 genes related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

Table S1.  Basic characteristics of clinical feature: 'DAYS_TO_DEATH_OR_LAST_FUP'

DAYS_TO_DEATH_OR_LAST_FUP Duration (Months) 0-211.2 (median=24.7)
  censored N = 26
  death N = 6
     
  Significant markers N = 3
  associated with shorter survival NA
  associated with longer survival NA
List of 3 genes differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of 3 genes significantly associated with 'Time to Death' by Cox regression test. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.

logrank_P Q C_index
SQSTM1|P62-LCK-LIGAND 0.00227 0.24 0.189
TTF1|TTF1 0.00325 0.24 0.443
YBX1|YB-1_PS102 0.00367 0.24 0.746
Clinical variable #2: 'YEARS_TO_BIRTH'

No gene related to 'YEARS_TO_BIRTH'.

Table S3.  Basic characteristics of clinical feature: 'YEARS_TO_BIRTH'

YEARS_TO_BIRTH Mean (SD) 54.82 (14)
  Significant markers N = 0
Clinical variable #3: 'TUMOR_TISSUE_SITE'

No gene related to 'TUMOR_TISSUE_SITE'.

Table S4.  Basic characteristics of clinical feature: 'TUMOR_TISSUE_SITE'

TUMOR_TISSUE_SITE Labels N
  ADRENAL 1
  ASCITES/PERITONEUM 1
  BONE 2
  BRAIN 2
  BREAST 1
  COLON 2
  LIVER 1
  OTHER EXTRANODAL SITE 1
  PAROTID GLAND 1
  SMALL INTESTINE 3
  STOMACH 2
  THYROID 1
     
  Significant markers N = 0
Clinical variable #4: 'GENDER'

5 genes related to 'GENDER'.

Table S5.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 15
  MALE 18
     
  Significant markers N = 5
  Higher in MALE 5
  Higher in FEMALE 0
List of 5 genes differentially expressed by 'GENDER'

Table S6.  Get Full Table List of 5 genes differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
EEF2|EEF2 217 0.003212 0.244 0.8037
NF2|NF2 216 0.003609 0.244 0.8
RPS6KA1|P90RSK_PT359_S363 56 0.004537 0.244 0.7926
ESR1|ER-ALPHA 57 0.005078 0.244 0.7889
RAF1|C-RAF_PS338 60 0.007069 0.271 0.7778
Clinical variable #5: 'RADIATION_THERAPY'

4 genes related to 'RADIATION_THERAPY'.

Table S7.  Basic characteristics of clinical feature: 'RADIATION_THERAPY'

RADIATION_THERAPY Labels N
  NO 27
  YES 5
     
  Significant markers N = 4
  Higher in YES 4
  Higher in NO 0
List of 4 genes differentially expressed by 'RADIATION_THERAPY'

Table S8.  Get Full Table List of 4 genes differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
CHEK2|CHK2_PT68 10 0.003093 0.243 0.9259
CHEK2|CHK2 11 0.003656 0.243 0.9185
NFKB1|NF-KB-P65_PS536 123 0.004311 0.243 0.9111
STAT5A|STAT5-ALPHA 122 0.005069 0.243 0.9037
Clinical variable #6: 'HISTOLOGICAL_TYPE'

No gene related to 'HISTOLOGICAL_TYPE'.

Table S9.  Basic characteristics of clinical feature: 'HISTOLOGICAL_TYPE'

HISTOLOGICAL_TYPE Labels N
  DIFFUSE LARGE B-CELL LYMPHOMA (DLBCL) NOS (ANY ANATOMIC SITE NODAL OR EXTRANODAL) 27
  PRIMARY DLBCL OF THE CNS 2
  PRIMARY MEDIASTINAL (THYMIC) DLBCL 4
     
  Significant markers N = 0
Clinical variable #7: 'RACE'

13 genes related to 'RACE'.

Table S10.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  ASIAN 16
  WHITE 17
     
  Significant markers N = 13
  Higher in WHITE 13
  Higher in ASIAN 0
List of top 10 genes differentially expressed by 'RACE'

Table S11.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

W(pos if higher in 'WHITE') wilcoxontestP Q AUC
PEA15|PEA15 228 0.0009807 0.156 0.8382
PXN|PAXILLIN 224 0.001622 0.156 0.8235
YAP1|YAP_PS127 217 0.003734 0.222 0.7978
CCNE1|CYCLIN_E1 60 0.006535 0.222 0.7794
FOXM1|FOXM1 60 0.006535 0.222 0.7794
BAP1|BAP1-C-4 61 0.007283 0.222 0.7757
MSH2|MSH2 62 0.008106 0.222 0.7721
ERBB3|HER3 66 0.0123 0.295 0.7574
ARAF|A-RAF_PS299 68 0.01504 0.298 0.75
CCNB1|CYCLIN_B1 71 0.02016 0.298 0.739
Clinical variable #8: 'ETHNICITY'

One gene related to 'ETHNICITY'.

Table S12.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 7
  NOT HISPANIC OR LATINO 26
     
  Significant markers N = 1
  Higher in NOT HISPANIC OR LATINO 1
  Higher in HISPANIC OR LATINO 0
List of one gene differentially expressed by 'ETHNICITY'

Table S13.  Get Full Table List of one gene differentially expressed by 'ETHNICITY'

W(pos if higher in 'NOT HISPANIC OR LATINO') wilcoxontestP Q AUC
CHEK2|CHK2 c("168", "0.0007549") c("168", "0.0007549") 0.145 0.9231
Methods & Data
Input

  • Expresson data file = DLBC-TP.rppa.txt

  • Clinical data file = DLBC-TP.merged_data.txt

  • Number of patients = 33

  • Number of genes = 192

  • Number of clinical features = 8

Selected clinical features

  • Further details on clinical features selected for this analysis, please find a documentation on selected CDEs (Clinical Data Elements). The first column of the file is a formula to convert values and the second column is a clinical parameter name.

  • Survival time data

    • Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.

      • if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'

      • if 'vital_status'==0(alive),

        • if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'

        • if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.

      • if 'vital_status'==NA,excludes this case in survival analysis and report the case.

    • cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .

  • This analysis excluded clinical variables that has only NA values.

Survival analysis

For survival clinical features, logrank test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values comparing quantile intervals using the 'coxph' function in R. Kaplan-Meier survival curves were plotted using quantile intervals at c(0, 0.25, 0.50, 0.75, 1). If there is only one interval group, it will not try survival analysis.

Correlation analysis

For continuous numerical clinical features, Spearman's rank correlation coefficients (Spearman 1904) and two-tailed P values were estimated using 'cor.test' function in R

Wilcoxon rank sum test (Mann-Whitney U test)

For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References
[1] Andersen and Gill, Cox's regression model for counting processes, a large sample study, Annals of Statistics 10(4):1100-1120 (1982)
[2] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[3] Mann and Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Annals of Mathematical Statistics 18 (1), 50-60 (1947)
[4] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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